Reporting adherence, validity and physical activity measures of wearable activity trackers in medical research: A systematic review

Chan, Alexandre; Chan, Daniella; Lee, Hui; Ng, Chiu Chin; Yeo, Angie Hui Ling

doi:10.1016/j.ijmedinf.2022.104696

Cited by 59 publications

(35 citation statements)

References 52 publications

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“…Adherence to wearing activity trackers was high, suggesting these devices are feasible and acceptable to participants. However, there was no standardized method for reporting wearable activity tracker outcomes, including adherence, validity, and physical activity measures [ 50 ]. The heterogeneous reporting of methods and results among studies using wearable physical activity trackers makes it difficult to compare findings across studies.…”

Section: Discussionmentioning

confidence: 99%

mHealth Interventions to Promote a Healthy Diet and Physical Activity among Cancer Survivors: A Systematic Review of Randomized Controlled Trials

Wang

Langlais

Kenfield

et al. 2022

Cancers

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Background: Technology-based interventions are increasingly used to improve physical activity (PA) and diet. Methods: We conducted a systematic review of randomized controlled trials (RCTs) published up to August 2021 that tested mobile health (mHealth) PA and/or dietary interventions among cancer survivors and reported on the feasibility, satisfaction, behavioral change, and/or quality of life (QOL) outcomes. Results: In total, 61 articles were identified on PubMed, and 23 of those met the inclusion criteria. The most common cancers were breast (n = 1000), prostate (n = 713), and colorectal (n = 650). Participants were predominantly White (median: 84%, interquartile range (IQR): 20%) and college-educated (58%). The interventions varied, but the most common combination of components (six studies) was a website/mobile app with an activity tracker and coaching. In terms of duration, 70% (n = 16) of the interventions lasted 12 weeks. The median total tracker wear was 87% of the study days (IQR: 6%) and the median text-message reply rate was 73% (IQR 4%). Most participants (median: 87%; IQR: 16%) were satisfied with at least one intervention component. Eleven out of 18 studies examining behavioral change reported significant between-group differences and six out of 11 studies examining QoL reported significant improvements. Conclusions: mHealth interventions are a promising approach to improving the PA and diets of cancer survivors. Research in racially/ethnically and socioeconomically diverse populations is needed.

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Section: Discussionmentioning

confidence: 99%

mHealth Interventions to Promote a Healthy Diet and Physical Activity among Cancer Survivors: A Systematic Review of Randomized Controlled Trials

Wang

Langlais

Kenfield

et al. 2022

Cancers

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“…We performed several steps to ensure the quality and completeness of data before initiating the clinical trial. It is essential that you take these steps to ensure that you can report adherence and physical activity measures at the conclusion of your study ( 20 ).…”

Section: Resultsmentioning

confidence: 99%

Leveraging Mobile-Based Sensors for Clinical Research to Obtain Activity and Health Measures for Disease Monitoring, Prevention, and Treatment

Dandapani

Davoodi

Joerg

et al. 2022

Front. Digit. Health

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Clinical researchers are using mobile-based sensors to obtain detailed and objective measures of the activity and health of research participants, but many investigators lack expertise in integrating wearables and sensor technologies effectively into their studies. Here, we describe the steps taken to design a study using sensors for disease monitoring in older adults and explore the benefits and drawbacks of our approach. In this study, the Geriatric Acute and Post-acute Fall Prevention Intervention (GAPcare), we created an iOS app to collect data from the Apple Watch's gyroscope, accelerometer, and other sensors; results of cognitive and fitness tests; and participant-entered survey data. We created the study app using ResearchKit, an open-source framework developed by Apple for medical research that includes neuropsychological tests (e.g., of executive function and memory), gait speed, balance, and other health assessments. Data is transmitted via an Application Programming Interface (API) from the app to REDCap for researchers to monitor and analyze in real-time. Employing the lessons learned from GAPcare could help researchers create study-tailored research apps and access timely information about their research participants from wearables and smartphone devices for disease prevention, monitoring, and treatment.

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“…Exploring baseline motivation has been common practice, but Sperandei’s results indicated that the potential changes in motivation during intervention could be highly significant, and that looking at motivation alone does not seem sufficient to promote adherence [ 12 ]. As far as technology is concerned, we now know that app glitches are able to profoundly influence app adherence in a negative manner [ 8 ], yet researchers seem to be lacking a consensus of conceptualization for adherence [ 7 , 8 , 12 , 25 ] and other related terms.…”

Section: Introductionmentioning

confidence: 99%

“…More recently, fitness trackers, pedometers and accelerometers have become popular instruments for obtaining reliable estimates of PA [ 26 ]. Some of these technologies, alone or in combination with others, allow researchers to measure not only wear duration and step count, but also other relevant factors related to exercise, such as activity intensity and energy expenditure [ 25 ]. Even for activity tracking publications, heterogeneity in the analyses and reporting of data has been observed—and authors have recommended that minimum reporting thresholds be determined in the future [ 25 ].…”

Section: Introductionmentioning

confidence: 99%

Adherence to eHealth-Delivered Exercise in Adults with no Specific Health Conditions: A Scoping Review on a Conceptual Challenge

Fuente-Vidal

Guerra-Balic

Roda-Noguera

et al. 2022

IJERPH

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Adherence has emerged as a focal point and critical determinant of success for physical activity interventions. The term is used for both traditional and digital interventions, and for prescribed and nonprescribed activities. Many other terms for adherence are being used interchangeably, as there is no consensus on its precise conceptualization. This scoping review aimed to advance the definition of adherence to eHealth programs, specifically for the adult population with no specific health conditions. A total of 2983 papers, published between 1 January 2016 and 13 March 2022, were retrieved from different databases (including grey literature). Of those, 13 studies met the eligibility criteria and were included for review. The selected studies used a wide array of technologies and consisted mainly of exercise interventions. Most of the reviewed publications contemplated exercise adherence as a percentage of expected dose. Most (8 out of 13) studies neither assessed nor specified an expected use of the involved technology. Results suggest a need for homogeneity in the conceptualization of adherence to physical activity and exercise, including those interventions delivered digitally.

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Reporting adherence, validity and physical activity measures of wearable activity trackers in medical research: A systematic review

Cited by 59 publications

References 52 publications

mHealth Interventions to Promote a Healthy Diet and Physical Activity among Cancer Survivors: A Systematic Review of Randomized Controlled Trials

mHealth Interventions to Promote a Healthy Diet and Physical Activity among Cancer Survivors: A Systematic Review of Randomized Controlled Trials

Leveraging Mobile-Based Sensors for Clinical Research to Obtain Activity and Health Measures for Disease Monitoring, Prevention, and Treatment

Adherence to eHealth-Delivered Exercise in Adults with no Specific Health Conditions: A Scoping Review on a Conceptual Challenge

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